Evaluation of global NWP data on Convection Environment over the Data-Sparse Northern South China Sea by Long-Term Observations

The coastal regions of southern China experience the country's most frequent convective weather. Accurately representing the low-level upstream atmospheric state over the data-sparse South China Sea (SCS) is crucial for reliable convection predictions in numerical models. Utilizing ten years of radiosonde observations launched over the SCS, this study presents the upstream offshore convective environments and evaluates the global model data performance including NCEP FNL, ERA5, CRA-40, JRA-3Q, and MERRA-2. Results show that thermodynamic state variables such as temperature and humidity exhibit greater biases than kinetic variables, particularly at low levels. Deeper-layer parameters exhibit smaller uncertainties, especially wind-related variables, while moisture-related parameters have the largest uncertainties, compared to shallower-layer parameters. All model data tend to underestimate the conditional instability and equilibrium level, while overestimating the condensation level, storm relative helicity (SRH), with minimal bias in lapse rate, convective inhibition, vertical wind shear (VWS) and mean winds. These biases primarily arise from the model data’s underestimation of temperature and moisture below 700 hPa and lower wind speeds below 950 hPa. Among the global models, CRA-40 performs best in dynamic parameters, with highest correlation and lowest mean absolute error in low-level winds, SRH, VWS, and mean winds. ERA5 excels in thermodynamic parameters. Additional convective-permitting numerical experiments indicate that minor initial condition errors over the upstream ocean significantly affect coastal rainfall production. The rainfall production on windward coasts is most sensitive to the low-level air temperature errors during nocturnal hours, while the rainfall over the PRD is most sensitive to the low-level wind errors.